To train our network, we collected a large Video Frame Interpolation Perceptual Similarity (VFIPS) dataset that contains 25,887 video triplets. Each video triplet contains two interpolated videos, their reference video, and the corresponding perceptual preference. Our dataset consists of various artifacts generated by eleven state-of-the-art video frame interpolation methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}. With this dataset, we train our video perceptual metric using Binary Cross Entropy (BCE) as the loss function in an end-to-end manner. Our experiments show that our metric outperforms existing metrics significantly when measuring video frame interpolation results.
